The present invention relates generally to organizing digital files and in particular, to organizing digital photos using optical parameters stored in camera metadata.
Much of the research in content based image classification and retrieval has focused on using two types of information sources: the pixel layer of an image and text present along with an image. It is known in the art of image retrieval systems to use various measures on the image features such as, color, texture, or shape. Other methods search images for local features such as edges, salient points, or objects. Algorithms have also been proposed which find scale and rotation invariant distinctive feature points in an image. These systems help in image matching and query by example. But image search using an example or low level features might be difficult and non-intuitive to some people. Rather image search using keywords has become more popular nowadays.
The prior art has used mapping on low level image features to semantically classify coherent image classes. It is known to use an algorithm on color and texture features to classify indoor outdoor images. One publication, “Content Based Hierarchical Classification of Vacation Images”. In Proc. IEEE Multimedia Computing and Systems, June 1999 (518-523), by Vailaya et al. discloses uses of a hierarchical structure to classify images into indoor-outdoor classes; then outdoor images into city and landscape. Other applications, such as image search engines rely on text, tags, or annotations to retrieve images.
Research using the annotations/tags or text accompanying an image in the prior art has been used to derive the human meta information from text accompanying the image. As disclosed in “Integration of visual and text-based approaches for the content labeling and classification of photographs” by Paek et al., they then combine the image features and text labels to classify photographs.
In some of the prior art, human agents are used to tag some images using predefined tags. An algorithm then predicts some tags on untagged images. This approach suffers from the fact that it is non trivial to define particular image classes especially for large heterogeneous image databases. Some may find that tagging an image to a particular class depends on the user's perception on a particular image.
Other prior art approaches have used the Optical Meta layer to classify and annotate images. Some use this layer to help improve classification using the pixel layer such as by using pixel values and optical metadata for sunset scene and indoor outdoor classification. Such approaches may choose the most significant cue using K-L divergence analysis. Others use a color, texture and camera metadata in a hierarchical way to classify indoor and outdoor images. But indoor-outdoor are considered by some very broad classes to actually help in any annotation or retrieval. Also these approaches lack the use of any strong reference to physics of vision (of why the images were being classified using the chosen cues). Further, the training sets used in the research have been artificially created for a specific purpose only.
As can be seen, there is a need for an improved method of classifying digital images for organization and retrieval that exploits inherent optical parameters for intuitive grouping by extracting similar optical features. Furthermore, it can be seen that a need exists for a method that automatically annotates digital images based on similar optical features.